Deep learning models are now emerging as powerful approaches for image recovery in a range of static inverse problems [1]}. Direct inversion strategies, which rely on a large convolutional neural network (CNN) to recover the images from undersampled data [2]}, [3]}, as well as model-based deep learning methods [4]}, [5]}, [6]} that interleave smaller CNN blocks with data-consistency enforcing optimization modules, have been introduced. By enforcing the data consistency, model-based methods can offer improved image quality over direct inversion strategies. Unfortunately, dynamic MRI and parametric MRI schemes often require the recovery of a large number of image frames; the direct application of the unrolled model-based deep learning schemes to the above setting is severely limited due to the high memory demand and computational complexity of current methods. Current strategies are either restricted to fewer time frames [7]} or often have to use small networks [8]}, [9]}. Another challenge associated with these schemes is the lack of fully sampled training data for training these models, especially in the free-breathing and ungated mode.
